Using Narrative Construction to Prepare the Ground for Hermeneutic Dialogue
Bibliographic record
Abstract
Hermeneutic research methodologists have recommended that when conducting interviews investigators should minimize the imposition of their pre-understandings on the conversation, as this would restrict the range of possible understandings that dialogue may produce. Instead, the phenomenon under investigation should determine the direction of conversation. This position paper argues that the iterative construction of a narrative enables increasingly focused, in-depth discussions with experts in the clearing where the interlocutors' shared horizons of understanding converge. Hermeneutic dialogue involves navigating one’s own pre-understanding and exploring new branches of possible understandings that emerge from conversation. This navigation demands reflexivity and adaptability, as well as an openness toward the complexity of a living understanding informed by a diversity of perspectives. To illustrate how narrative construction informs an increasingly refined understanding, and how this understanding subsequently frames dialogue with interview subjects, this paper draws on a historical case study of electronic medical records implementation policy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".